Qwen 2.5 7B vs DeepSeek V3
Architecture Comparison
SpecQwen 2.5 7BDeepSeek V3
TypeDENSEMOE
Total Parameters7.6B671B
Active Parameters7.6B37B
Layers2861
Hidden Dimension3,5847,168
Attention Heads28128
KV Heads41
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A256
Active ExpertsN/A8
Memory Requirements
PrecisionQwen 2.5 7BDeepSeek V3
BF16 Weights15.2 GB1342.0 GB
FP8 Weights7.6 GB671.0 GB
INT4 Weights3.8 GB335.5 GB
KV-Cache / Token57344 B31232 B
Activation Estimate1.00 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPUN/A
L40S1 GPUN/A
Quality Benchmarks
BenchmarkQwen 2.5 7BDeepSeek V3
Overall7086
MMLU74.287.1
HumanEval42.865.0
GSM8K82.089.3
MT-Bench79.087.0
Qwen 2.5 7B
MMLU
74.2
HumanEval
42.8
GSM8K
82.0
MT-Bench
79.0
DeepSeek V3
MMLU
87.1
HumanEval
65.0
GSM8K
89.3
MT-Bench
87.0
Capabilities
FeatureQwen 2.5 7BDeepSeek V3
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Qwen 2.5 7B)
$0.20/M
Input: $0.20/M
Cheapest Output (DeepSeek V3)
$0.42/M
Input: $0.28/M
| Provider | Qwen 2.5 7B In $/M | Out $/M | DeepSeek V3 In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.20 | $0.20 | $0.50 | $2.80 |
| fireworks | $0.20 | $0.20 | — | — |
| deepseek | — | — | $0.28 | $0.42 |
Recommendation Summary
- ‣DeepSeek V3 scores higher on overall quality (86 vs 70).
- ‣Qwen 2.5 7B is cheaper per output token ($0.20/M vs $0.42/M).
- ‣Qwen 2.5 7B has a smaller memory footprint (15.2 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 2.5 7B uses DENSE architecture while DeepSeek V3 uses MOE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek V3 is stronger at code generation (HumanEval: 65.0 vs 42.8).
- ‣DeepSeek V3 is better at math reasoning (GSM8K: 89.3 vs 82.0).